A decision-making framework for selecting an MBSE language–A case study to ship pilotage

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The increasing system complexity due to technological advances in recent decades requires the implementation of Model-Based System Engineering (MBSE) languages. While MBSE languages can support in creating detailed and precise system descriptions, requirements, functions, and component interactions, the development of several modelling languages has formed a challenge for system engineers to select a suitable modelling language for complex operations. Since the implementation of MBSE modelling languages is extensive and requires high resources, system engineers need to select the most suitable one for their project. Furthermore, the prepared system models are utilized by different end-users such as system engineers, operators, and marketing professionals. As a result, it is necessary to integrate the perception of end-users in the modelling language selection process. Hence, a decision-making support framework needs to be developed, which will incorporate the opinions of end-users in the selection process. Correspondingly, this study proposes a Multi-Criteria Decision-Making framework aimed to select the most appropriate and practical modelling language. The framework integrates the end-user’s perception in the selection process using the Technology Acceptance Model and reckons the MBSE language features as comparison criteria. To analyze the data collected from the end-users, integration of Multinomial Process Tree modelling and Bayesian inference is developed. The applicability of the proposed model was tested in a ship pilotage operation case study. The results show that the framework can support system engineers during the initial selection process of the MBSE modelling language.
Original languageEnglish
Article number116451
Number of pages15
JournalExpert Systems with Applications
Publication statusPublished - 1 May 2022
MoE publication typeA1 Journal article-refereed


  • Decision-making framework
  • Model-based system engineering
  • Multinomial process tree
  • Bayesian Inference


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